Zobrazeno 1 - 10
of 1 019
pro vyhledávání: '"Satyapriya A"'
Large language models (LLMs) trained with Reinforcement Learning from Human Feedback (RLHF) have demonstrated remarkable capabilities, but their underlying reward functions and decision-making processes remain opaque. This paper introduces a novel ap
Externí odkaz:
http://arxiv.org/abs/2410.12491
Autor:
Krishna, Satyapriya, Krishna, Kalpesh, Mohananey, Anhad, Schwarcz, Steven, Stambler, Adam, Upadhyay, Shyam, Faruqui, Manaal
Large Language Models (LLMs) have demonstrated significant performance improvements across various cognitive tasks. An emerging application is using LLMs to enhance retrieval-augmented generation (RAG) capabilities. These systems require LLMs to unde
Externí odkaz:
http://arxiv.org/abs/2409.12941
Autor:
Verma, Apurv, Krishna, Satyapriya, Gehrmann, Sebastian, Seshadri, Madhavan, Pradhan, Anu, Ault, Tom, Barrett, Leslie, Rabinowitz, David, Doucette, John, Phan, NhatHai
Creating secure and resilient applications with large language models (LLM) requires anticipating, adjusting to, and countering unforeseen threats. Red-teaming has emerged as a critical technique for identifying vulnerabilities in real-world LLM impl
Externí odkaz:
http://arxiv.org/abs/2407.14937
The trustworthiness of Large Language Models (LLMs) refers to the extent to which their outputs are reliable, safe, and ethically aligned, and it has become a crucial consideration alongside their cognitive performance. In practice, Reinforcement Lea
Externí odkaz:
http://arxiv.org/abs/2404.18870
Autor:
Peng, Bo, Goldstein, Daniel, Anthony, Quentin, Albalak, Alon, Alcaide, Eric, Biderman, Stella, Cheah, Eugene, Du, Xingjian, Ferdinan, Teddy, Hou, Haowen, Kazienko, Przemysław, GV, Kranthi Kiran, Kocoń, Jan, Koptyra, Bartłomiej, Krishna, Satyapriya, McClelland Jr., Ronald, Lin, Jiaju, Muennighoff, Niklas, Obeid, Fares, Saito, Atsushi, Song, Guangyu, Tu, Haoqin, Wirawan, Cahya, Woźniak, Stanisław, Zhang, Ruichong, Zhao, Bingchen, Zhao, Qihang, Zhou, Peng, Zhu, Jian, Zhu, Rui-Jie
We present Eagle (RWKV-5) and Finch (RWKV-6), sequence models improving upon the RWKV (RWKV-4) architecture. Our architectural design advancements include multi-headed matrix-valued states and a dynamic recurrence mechanism that improve expressivity
Externí odkaz:
http://arxiv.org/abs/2404.05892
Autor:
Akhtar, Mubashara, Benjelloun, Omar, Conforti, Costanza, Foschini, Luca, Giner-Miguelez, Joan, Gijsbers, Pieter, Goswami, Sujata, Jain, Nitisha, Karamousadakis, Michalis, Kuchnik, Michael, Krishna, Satyapriya, Lesage, Sylvain, Lhoest, Quentin, Marcenac, Pierre, Maskey, Manil, Mattson, Peter, Oala, Luis, Oderinwale, Hamidah, Ruyssen, Pierre, Santos, Tim, Shinde, Rajat, Simperl, Elena, Suresh, Arjun, Thomas, Goeffry, Tykhonov, Slava, Vanschoren, Joaquin, Varma, Susheel, van der Velde, Jos, Vogler, Steffen, Wu, Carole-Jean, Zhang, Luyao
Data is a critical resource for machine learning (ML), yet working with data remains a key friction point. This paper introduces Croissant, a metadata format for datasets that creates a shared representation across ML tools, frameworks, and platforms
Externí odkaz:
http://arxiv.org/abs/2403.19546
The development of Large Language Models (LLMs) has notably transformed numerous sectors, offering impressive text generation capabilities. Yet, the reliability and truthfulness of these models remain pressing concerns. To this end, we investigate it
Externí odkaz:
http://arxiv.org/abs/2402.06625
Autor:
Casper, Stephen, Ezell, Carson, Siegmann, Charlotte, Kolt, Noam, Curtis, Taylor Lynn, Bucknall, Benjamin, Haupt, Andreas, Wei, Kevin, Scheurer, Jérémy, Hobbhahn, Marius, Sharkey, Lee, Krishna, Satyapriya, Von Hagen, Marvin, Alberti, Silas, Chan, Alan, Sun, Qinyi, Gerovitch, Michael, Bau, David, Tegmark, Max, Krueger, David, Hadfield-Menell, Dylan
Publikováno v:
The 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT '24), June 3-6, 2024, Rio de Janeiro, Brazil
External audits of AI systems are increasingly recognized as a key mechanism for AI governance. The effectiveness of an audit, however, depends on the degree of access granted to auditors. Recent audits of state-of-the-art AI systems have primarily r
Externí odkaz:
http://arxiv.org/abs/2401.14446
Autor:
Krishna, Satyapriya
Large Language Models (LLMs) have demonstrated remarkable capabilities in performing complex cognitive tasks. However, their complexity and lack of transparency have raised several trustworthiness concerns, including the propagation of misinformation
Externí odkaz:
http://arxiv.org/abs/2311.02801
Recent advancements in Large Language Models (LLMs) have demonstrated exceptional capabilities in complex tasks like machine translation, commonsense reasoning, and language understanding. One of the primary reasons for the adaptability of LLMs in su
Externí odkaz:
http://arxiv.org/abs/2310.05797